How to Use Deep Learning Even If You Lack the Data?
It’s a tricky task. To train a computer algorithm when you don’t have any data. Some would say, it’s impossible – but at a time where data is so sensitive, it’s a common hurdle for a business to face. Imagine, you needed to monitor your database for identity theft. Say, by using personal information that, for legal reasons, you cannot share.
What is Deep Learning?
Deep learning is a form of machine learning. It’s a technique that teaches computers to do what people do – that is, to learn by example.
Synthetic Data
At DLabs.AI, we’re working with a client who needs to detect logos on images. Yet, they don’t have the dataset to train the deep learning algorithm, so we’re creating fake – or synthetic – data for them.
Pros and Cons of Synthetic Data
There are several reasons beyond privacy that real data may not be an option. The most obvious? Limited resources. If a company wants to train an algorithm with real images, it requires a manual process to label the key elements (in our example, the logo) and that quickly gets expensive.
Benefits of Synthetic Data
By generating synthetic data, you get two clear benefits.
Cheaper
In the DLabs.AI example, as we embedded the logo ourselves, we knew the precise position of the logo on every image – so we could label it automatically. By generating synthetic data, we instantly saved on labor costs.
Quicker
Plus, once we had created our first data point, it didn’t take long to duplicate the record to create a catalog of thousands of correctly-labeled images.
Dynamic
Moreover, when you train a model on synthetic data, then deploy it to production to analyze real data, you can use the production data (in our client’s case – real imagery) to continually improve the performance of the deep learning model.
Drawbacks of Synthetic Data
Synthetic data does have its drawbacks; the most difficult to mitigate being authenticity.
3 Steps to Know If Deep Learning Can Help Your Business
Clients contact us every week to ask "can deep learning help my business?" but then feel overwhelmed by the apparent complexity of the technique.
To keep things as simple as possible, we approach the question in three steps.
Step 1: Develop a Research & Development Outline
We investigate the kinds of products or algorithms that we could use to solve your problem. We review the latest scientific research on the subject to see if we can use any particular findings – or if there is an open-source implementation we can adapt to your case.
Step 2: Confirm if There’s Business Value
We outline an integration model to confirm we can deliver the expected value. By this stage, both parties should have a rough idea of what’s to come, so we avoid nasty surprises down the line – like a client with a solution she doesn’t actually want.
Step 3: Only Once We’re Aligned on the Outcome, Do We Train the Algorithm
This is where it gets technical.
Connect With DLabs.AI
Artificial Intelligence is changing the world as we know it as businesses in every sector achieve the seemingly impossible. So ask yourself "can deep learning solve my problem as well?" With the development of DLabs’ synthetic approach, data is never the limit. If you’re interested in deep learning – now is the time to get in touch.
